Liquid back-mixing in
packed-bubble column reactors:
A
state-of-the-art correlation
Lamia Belfares1), Miryan
Cassanello2), Bernard P.A. Grandjean1) and Faïçal Larachi1)
1)
Department of Chemical Engineering and CERPIC
Université
Laval, Ste-Foy, Québec, Canada, G1K 7P4
Corresponding author: flarachi@gch.ulaval.ca
2)PINMATE, Departmento
de Industrias, Facultad de Ciencias Exactas y Naturales,
Universidad
de Buenos Aires, Ciudad Universitaria, 1428 Buenos Aires, Argentina
Catalysis Today
64, 321-332 (2001)
Abstract:
The extent of liquid back-mixing in gas-liquid cocurrent
upflow packed-bubble column reactors is quantified in
terms of an axial dispersion coefficient or its corresponding dimensionless Péclet number. Effects of reactor operating
conditions on the axial dispersion coefficient are not properly accounted for
by the available literature correlations wherein most often the Péclet number is expressed solely in terms of the gas and
liquid Reynolds numbers or superficial velocities. Based on the broadest
experimental databank (1322 measurements, 11 liquids, 4 gases, 28 packing
materials, 14 columns diameters, Newtonian, non-Newtonian, aqueous, organic,
coalescing and non-coalescing liquids, high pressure, bubble and pulsing flow
regime conditions), a state-of-the-art liquid axial dispersion coefficient
correlation is obtained by combining neural network modeling and dimensional
analysis. Thorough qualitative and quantitative analyses of the
constructed databank demonstrate the robustness of the proposed correlation to
restore the variety of trend variations of liquid Péclet
numbers reported in the literature.
You can get
the pecletflb.zip file that contains the
following source codes:
- Fortran (to
compute the liquid Peclet number)
- Excel worksheet simulator for flooded-bed reactor
( to compute liquid Peclet number, pressure drop and
liquid saturation)
(and the updated version flbsimul-peclet_update.zip
).
You may also download our Excel worksheet Trickle-bed simulator to simulate mass transfer, pressure
drop, liquid holdup and flow regime transition.
The neural
correlation was developped with the software NNFit